Comparative analysis of the predictive capabilities of some machine learning models: A case study using wind speed data

The widespread use of fossil fuels for global energy production significantly contributes to global warming. This study presents a comparative analysis of various machine learning models, which are the long short-term memory (LSTM) network, support vector regression (SVR), and gradient boosting method (GBM). Gaussian process regression (GPR) is a benchmark model across different forecasting horizons. The study uses South African wind speed data from 1 January 2018 to 31 December 2021, sourced from the Western Cape province. The dataset underwent preprocessing, and diverse feature selection techniques were implemented to enhance model accuracy. Performance evaluation of the models was done using mean absolute error (MAE), root mean squared error (RMSE), and mean absolute scaled error (MASE). Results indicate that SVR exhibits superior accuracy to other models for two distinct forecast horizons (h = 670 and h = 1339), respectively. Additionally, GPR surpasses other models for the forecasting horizon h = 224. This study provides insights into the comparative strengths and weaknesses of different machine learning models for wind speed prediction, which could be useful in selecting an appropriate model for future applications in renewable energy and weather forecasting. Potential areas for future research include improving prediction accuracy via ensemble deep learning algorithms and incorporating additional meteorological variables. Moreover, investigating temporal dynamics, broadening geographical coverage and integrating uncertainty quantification methods can improve wind speed prediction, thereby facilitating more effective renewable energy planning and decision-making processes